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2ec9b02 bf9977d 2ec9b02 bf9977d 2ec9b02 f688e19 2ec9b02 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 | import gradio as gr
import torch
from threading import Thread
from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer, BitsAndBytesConfig
MODEL_ID = "huihui-ai/Huihui-Qwen3.5-35B-A3B-abliterated"
print("Loading tokenizer...")
tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
print("Loading model (4-bit quantized)...")
model = AutoModelForCausalLM.from_pretrained(
MODEL_ID,
quantization_config=BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_compute_dtype=torch.bfloat16,
bnb_4bit_quant_type="nf4",
bnb_4bit_use_double_quant=True,
),
device_map="auto",
dtype=torch.bfloat16,
)
print("Model loaded!")
def chat(message, history):
messages = [{"role": "system", "content": "You are a helpful assistant."}]
for user_msg, bot_msg in history:
messages.append({"role": "user", "content": user_msg})
if bot_msg:
messages.append({"role": "assistant", "content": bot_msg})
messages.append({"role": "user", "content": message})
text = tokenizer.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True, enable_thinking=False
)
inputs = tokenizer(text, return_tensors="pt").to(model.device)
streamer = TextIteratorStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
thread = Thread(
target=model.generate,
kwargs=dict(
**inputs,
max_new_tokens=2048,
temperature=0.7,
top_k=20,
top_p=0.95,
do_sample=True,
streamer=streamer,
),
)
thread.start()
partial = ""
for token in streamer:
partial += token
yield partial
demo = gr.ChatInterface(
chat,
title="Huihui-Qwen3.5-35B-A3B Abliterated",
description="Chat with the abliterated Qwen3.5-35B-A3B model (4-bit quantized, uncensored)",
)
demo.launch(server_name="0.0.0.0", server_port=7860)
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